Passenger-Centric Integrated Timetable Rescheduling for High-Speed Railways Under Multiple Disruptions
In high-speed railway networks, multiple spatiotemporal correlated disruptions often cause passenger trip failures and delay propagation. Conventional single-disruption rescheduling strategies struggle to resolve such cross-line conflicts, necessitating an integrated, passenger-centric rescheduling framework for multiple correlated disruptions. This paper proposes a mixed-integer linear programming (MILP) model to minimize total passenger delay time and trip failures under scenarios involving disruptions that are geographically dispersed but operationally interconnected. Two rescheduling mechanisms are introduced: a stepwise rescheduling method, which iteratively applies single-disruption models to optimize local problems, and an integrated rescheduling method, which simultaneously considers the global impact of all disruptions. Case studies on a real-world China’s high-speed railway network (29 stations, 42 trains, and 36,193 passenger trips) demonstrate that the proposed integrated rescheduling method reduces total passenger delays by 13% and trip failures by 67% within a 300 s computational threshold. By systematically coordinating spatiotemporal interdependencies among disruptions, this approach enhances network accessibility and service quality while ensuring operational safety, providing theoretical foundations for intelligent railway rescheduling.
Authors
- Michael C. H. Hui
- Pengcheng Wen (ORCID: https://orcid.org/0000-0003-3385-0549)
- Yong‐Sheng Chen (ORCID: https://orcid.org/0000-0002-5581-850X)
- Letian Fan
- Ke Qiao (ORCID: https://orcid.org/0000-0003-0707-5630)
- Tiqiang Shen
Institutions
- Tangshan College (CN)
- Beijing Jiaotong University (CN)
- Dalian Jiaotong University (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3390/su17125624
- Citations
- 1
- Primary Topic
- Railway Systems and Energy Efficiency
- Type
- article
- Field-Weighted Citation Impact
- 1.24
Funders
- National Natural Science Foundation of China